Particle emission tomography

ABSTRACT

The present invention provides autoradiography methods and systems for imaging via the detection of alpha particles, beta particles, or other charged particles. Embodiments of the methods and systems provide high-resolution 3D imaging of the distribution of a radioactive probe, such as a radiopharmaceutical, on a tissue sample. Embodiments of the present methods and systems provide imaging of tissue samples by reconstruction of a 3D distribution of a source of particles, such as a radiopharmaceutical. Embodiments of the methods and systems provide tomographic methods including microtomography, macrotomography, cryomicrotomography and cryomacrotomography.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a U.S. National Stage Application filed under 35U.S.C. § 371 of International Application No. PCT/US2015/060198, filedNov. 11, 2015, which claims the benefit of priority from U.S.Provisional Patent Application No. 62/078,562 filed Nov. 12, 2014, andU.S. Provisional Application Number 62/199,904 filed on Jul. 31, 2015,each of which is hereby incorporated by reference in its entirety to theextent not inconsistent herewith.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with governmental support under Grants No. R01EB000803 and No. P41 EB002035 awarded by NIH. The government has certainrights in the invention.

BACKGROUND

Autoradiography is a well-developed technique for imaging in the contextof clinical medicine and research on biological processes. In thistechnique, a radioactive probe is administered to a patient or a subjector a tissue to provide an internal source of radiation, thusdistinguishing this method from conventional radiography in which anexternal source of radiation is employed. Autoradiography is mostcommonly used for imaging of ex vivo samples obtained from tissueadministered with a radioactive pharmaceutical. Thin slices (e.g., 5-50μm thick) of the sample are subsequently analyzed using ahigh-resolution imaging detector sensitive to charged particles (e.g.,alpha particles, beta particles and/or Auger electrons) emitted by theradioactive pharmaceutical. These techniques provide 2D imagesexhibiting high spatial resolution capable of resolving the distributionof a radioactive pharmaceutical at the cellular or subcellular level.

Although autoradiography provides a valuable approach forhigh-resolution imaging of tissue, this technique is significantlylimited in its extension to 3D imaging of in vivo tissue. Whilereassembly of 2D slice image information to obtain a 3D image of asample is feasible, this application of autoradiography is laborintensive and practically limited due to distortion of thin film slicesintroduced by dehydration and/or in transferring them to an imagingdetector. Further, extension of conventional autoradiography to 3Dimaging requires sectioning of the sample into thin slices to providedepth information, thereby effectively limiting the technique toapplication of ex vivo tissue samples.

SUMMARY

The present invention provides autoradiography methods and systems forimaging via the detection of alpha particles, beta particles, or othercharged particles. Embodiments of the methods and systems providehigh-resolution 3D imaging of the distribution of a radioactive probe,such as a radiopharmaceutical, on a tissue sample. Embodiments of thepresent methods and systems provide imaging of tissue samples byreconstruction of a 3D distribution of a source of particles, such as aradiopharmaceutical. Embodiments of the methods and systems providetomographic methods including microtomography, macrotomography,cryomicrotomography and cryomacrotomography.

The present invention provides autoradiography methods and devices for3D imaging via the detection of beta particles or other chargedparticles. Embodiments of the present methods and systems providehigh-resolution 3D imaging of the distribution of a radioactive probe,such as a radioactive pharmaceutical, in an intact, unsectioned tissuesample without the need for physically slicing the sample into sections.Embodiments of the present methods and systems provide for 3D imaging ofin vivo tissue and ex vivo tissue via detection of particles from asingle side of the sample. Embodiments of the present methods andsystems provide for 3D imaging of living tissue including, for example,dynamic, time evolved imaging and characterization of a living tissuesample.

The disclosed systems and methods employ an autoradiographic imagingapproach where particles emitted by a radioactive composition within thetissue are detected to provide a plurality of position dependentsignals, for example, providing information characterizing individualtrajectories of the detected particles. In some embodiments, a chargedparticle track detector is used to independently detect particles at aplurality of positions along their respective trajectories. For example,suitable track detectors include scintillator-based detectors,microchannel plate-based image intensifiers coupled to a thickscintillation material or semiconductor charge-coupled devices (CCD),active pixel sensors in complementary metal-oxide-semiconductor (CMOS),or N-type metal-oxide-semiconductor (NMOS, Live MOS) technologies orother video camera type detector where the sensitive region, activeregion or depletion region is thick enough to stop the particle. Therecorded track can be analyzed to determine attributes of each tracksuch as the point at which the charged particle entered the thickdetector, the particle's direction at that point and the total energydeposited in that track. In embodiments, these attributes are used in aniterative tomographic reconstruction algorithm for accuratedetermination of a 3D image of the distribution of the source ofparticles within the tissue, for example, by determining positions anddirections of the detected particles interacting with a charged particletrack detector. In embodiments, characterization of the positions anddirections of particles entering a detector provides information usefulfor determining a distribution of the source of particles within thetissue using various methods. In some embodiments, a particle transportalgorithm is utilized, which estimates, simulates or otherwise accountsfor propagation processes that take place between a location and thepoint at which the particle interacts with the detector. In someembodiments, for example, a maximum likelihood expectation maximizationalgorithm is used to accurately reconstruct a 3D image of thedistribution of a radiopharmaceutical in a sample from the positiondependent signals collected for the detected particles. Optionally, thedevices and methods of the invention are useful for not only detectingbeta particles, but other energetic particles, including alphaparticles, conversion electrons, auger electrons, electron-likeparticles and/or positrons.

In an aspect, provided are methods of reconstructing a 3D distributionof a source of the particles. In a specific embodiment, the inventionprovides s method for reconstructing a 3D distribution of a source ofparticles, the method comprising the steps of: (2) providing the sourceof particles from within a tissue sample, wherein the particles comprisebeta particles, alpha particles, positrons, or conversion electrons; (2)repeating, for each of a plurality of the particles from the source, thesteps of: (a) detecting the particle with a particle-processingdetector; (b) determining attributes of the particle; wherein theattributes include at least one of: (i) a two dimensional positioncorresponding to an interaction point where the particle interacts withthe particle-processing detector; and (ii) an energy that is depositedin the particle-processing detector by the particle; and (3) storing theattributes of the particle; thereby generating attributes for each ofthe plurality of particles from the source; and (4) reconstructing the3D distribution of the source of particles using at least a portion ofthe attributes for each of the plurality of particles. In an embodiment,a method of the invention includes determining both the attributes of:(i) a two dimensional position corresponding to an interaction pointwhere the particle interacts with the particle-processing detector; and(ii) an energy that is deposited in the particle-processing detector bythe particle

In an embodiment, for example, the interaction point corresponds to atwo-dimensional position that the particle interacts with an active areaof the two-dimensional detector. In an embodiment, the interaction pointcorresponds to a two-dimensional position that the particle interactswith an entrance face of the two-dimensional detector. In certainembodiments, the energy is a total energy that is deposited on thetwo-dimensional detector by the particle. In embodiments, the attributesfurther comprise a particle interaction time, the energy of the particleupon interacting with the detector or a direction of travel of theparticle.

In an exemplary embodiment, the provided method comprises a tomographicmethod, for example, microtomography, macrotomography,cryomicrotomography or cryomacrotomography. In certain embodiments, thesource of particles is present within a tissue sample. In embodiments,the source of particles comprise a distribution of a radiopharmaceuticalwithin the tissue sample. In certain embodiments, the method furthercomprises calculating the energy lost by the particle while traveling inthe tissue sample. In certain embodiments, the method further comprisescalculating the total distance traveled by the particle within thetissue.

In an exemplary embodiment, the provided method wherein a particle isemitted upon radioactive decay occurring in the tissue to be imaged, andhas an initial energy that is known. In an embodiment, a particle isemitted upon radioactive decay occurring in the tissue to be imaged, andhas an initial energy that is not known. In an embodiment, furthercomprising determining the energy and angle of the particle. In anembodiment, further comprising calculating an energy lost for each ofthe plurality of particles while traveling in the tissue sample. In anembodiment, a method further comprising calculating a distance traveledfor each of the plurality of particles within the tissue sample from theenergy lost by each of the particles.

In an exemplary embodiment, the provided method interaction pointcorresponds to a two-dimensional position that the particle interactswith an active area of the particle-processing detector. In anembodiment, a particle-processing detector is one that collects signalsfrom multiple sensors (usually pixels) and uses them to estimateattributes of the particle interaction. In an embodiment, theinteraction point corresponds to a two-dimensional position that theparticle interacts with an entrance face of the particle-processingdetector. In further embodiments, the attributes further comprises aparticle interaction time, the energy of the particle upon interactingwith the detector or a direction of travel of the particle. In anembodiment, the source of particles comprises a distribution of aradiopharmaceutical within the tissue sample.

In an exemplary embodiment, the provided method further comprising astep of administering the source of particles to a patient, subject ortissue, wherein the source of particles comprises a radiopharmaceutical.In embodiments, the 3D distribution of the source of particles comprisesa distribution of the radiopharmaceutical in the tissue. In embodiments,the tissue is in vivo tissue or ex vivo tissue.

In embodiments, the method wherein the source of particles is providedin a tissue sample having a thickness selected from the range of 1 μm to100 mm. In embodiments, the source of particles is located at a depthwithin a tissue selected from the range of 0 to 10 mm. In embodiments,the source of particles comprises radioactive compositions within livingtissue. In embodiments, the source of particles is provided in a tissuesample having a thickness selected from the range of 1 μm to 100 μm. Inembodiments, the source of particles is located at a depth within atissue selected from the range of 0 to 10 μm. Exemplary particlesinclude, but are not limited to, subatomic particles such as protons,neutrons and electrons, high-energy particles such as alpha particlesand beta particles, atomic nuclei, atoms and ions. As used herein,particles explicitly include alpha particles, beta particles, positrons,conversion electrons and Auger electrons.

In embodiments, the particle-processing detector is not a particle trackdetector. In embodiments, the particle-processing detector comprises asemiconductor detector. In embodiments, the semiconductor detectorprovides an energy resolution equal to or better than 10% of the totalenergy deposited by the particle in the particle-processing detector. Inembodiments, the semiconductor detector provides a position resolutionequal to or better than 10 μm. In embodiments, the particle-processingdetector comprises a hybrid semiconductor pixelated detector. Inembodiments, the particle-processing detector comprises a layer ofsemiconductor material comprising an active volume and a set of anodes;wherein the set of anodes is provided in electrical contact with a sideof the active volume opposite an entrance face of theparticle-processing detector. In embodiments, the semiconductor detectorhas a pixel array size between 8×8 and 1024×1024 pixels. In embodiments,the pixel measures between 1 μm×1 μm and 300 μm×300 μm. In embodiments,the semiconductor detector has a detection area selected over a range ofbetween 10 mm² to 100 cm². In embodiments, the semiconductor detectorprovides a subpixel spatial resolution greater than 750 nm for anequivalent 10 MeV alpha particle. In embodiments, the semiconductordetector is provided proximate to the source of particles.

In an exemplary embodiment, the method comprises a particle-processingdetector. In embodiments, the particle processing detector comprises asilicon sensor. In embodiments, the particle-processing detectorcomprises a scintillation camera. In embodiments, the scintillationcamera comprises a scintillation crystal, a PiN diode array, and a lightguide, wherein the light guide is a glass spacer. In embodiments, thePiN diode array comprises an array of PiN diodes. In embodiments, thePiN diode array has a pixel array size between 2×2 and 1024×1024 pixels.In embodiments, the scintillation crystal converts alpha particleabsorption to light emission. In embodiments, the light guide blursscintillation light into multiple pixels. In embodiments, thescintillation camera further comprises a charge-coupled device (CCD), ora complementary metal-oxide-semiconductor (CMOS) detector.

In exemplary embodiments, the methods further comprise additionalattributes for each particle including at least a portion of: a time, atissue sample, one or more angles characterizing a direction of travelalong the independent particle trajectory at the 2D position.

In embodiments, attributes comprise a set of parameters that areestimated for each particle as an estimated parameter vector and storedas entries in an attribute list, 3D grid of bins or a database. Inembodiments, estimation of the set of parameters is performed using amaximum-likelihood algorithm, such as a maximum-likelihood searchalgorithm. In embodiments, the generating step is performed using amaximum-likelihood algorithm, such as a list-mode maximum-likelihoodexpectation-maximization algorithm. In embodiments, the attributescomprise a set of parameters that are estimated for each particle as anestimated parameter vector and stored as entries in an attribute list,3D grid of bins or a database. In embodiments, the estimation of the setof parameters is performed using a maximum-likelihood algorithm. Inembodiments, for each particle independently, a point from which theparticle emits is constrained to lie within a spherical shell having athickness that is determined. In embodiments, the thickness isdetermined by a probability density function that is dependent on adetector energy response. In embodiments, the source of particles can begenerated by one or more isotopes In embodiments, the step ofreconstructing the 3D distribution of the source of particles is carriedout using an Ordered Subsets-Expectation Maximization (OSEM) algorithm,an Algebraic Reconstruction Technique (ART), or a Simultaneous IterativeReconstructive Technique (SIRT).

In embodiments, the step of reconstructing the 3D distribution of thesource of particles comprises calculating a probability density functionfor each of a plurality of locations within the source of particles. Inembodiments, the probability density function is calculated using one ormore computer models from list comprising: a Monte Carlo simulation, anordered subsets-expectation maximization (OSEM) algorithm, an AlgebraicReconstruction Technique (ART), or a Simultaneous IterativeReconstructive Technique (SIRT). In embodiments, the probability densityfunction for each location accounts for propagation of particles betweenthat location and the particle-processing detector. In embodiments, themethod comprising providing a visual display of the 3D distribution ofthe source of particles.

In an embodiment, for example, the method further comprises, for atleast a portion of the particles, independently measuring an imagecorresponding to a track of the particle interacting with theparticle-processing detector. In an embodiment, for example, the methodfurther comprises, for at least a portion of the particles, determiningone or more additional attributes of each of the particles using theimage corresponding to the track of the particle. In an embodiment, forexample, the one or more additional attributes are selected from thegroup consisting of a 2D position of a start of the particle track, adirection of travel of a particle at a point along the particle track,and a total energy deposited by a particle along a particle track. In anembodiment, for example, the one or more additional attributes are usedto construct the 3D distribution of the source of particles. In anembodiment, for example, the particle-processing detector comprises atrack detector.

In another aspect, the present invention provides devices forreconstructing a 3D distribution of a source of particles from within atissue sample, wherein the particles comprise beta particles, alphaparticles, positrons, or conversion electrons. An exemplary deviceembodiment comprises: a particle-processing detector for detecting theparticles; a processor positioned in data communication with theparticle-processing detector, wherein the processor is configured for:determining attributes of the particle; wherein the attributes includeat least one of: (i) a two dimensional position corresponding to aninteraction point where the particle interacts with theparticle-processing detector; (ii) an energy that is deposited in theparticle-processing detector by the particle; and storing the attributesof the particle; thereby generating attributes for each of the pluralityof particles from the source; and reconstructing the 3D distribution ofthe source of particles using at least a portion of the attributes foreach of the plurality of particles. In an embodiment, the deviceincludes a processor for determining both attributes of (i) a twodimensional position corresponding to an interaction point where theparticle interacts with the particle-processing detector; (ii) an energythat is deposited in the particle-processing detector by the particle.

In some embodiment, the detector configuration uses a fast camera, suchas a camera providing a frame speed capable of generating useful imagedata. For certain applications, for example, a camera used as a sensorin the present methods and systems is able to acquire frames, forexample having a size of 512×512 (or larger), at a frame rate of 35000frames per second or higher. In some embodiments, for example,estimation of position, energy, and direction of propagation of aparticle is achieved with accuracy if the signal (e.g., either lightflashes produced by the interaction between a particle and thescintillator, or secondary electrons by the interaction between aparticle and the detector itself) corresponding to different particlesare spatially well separated in the final detector images. In an idealcase, each image collected by the camera will contain zero or moreflashes of light all associated to no more than one particle. Ifmultiple flashes of light corresponding to different particles arepresent in a detector image, the image data may be exhibit “spatialpileup” (Furenlid, L. R.; Clarkson, E.; Marks, D. G.; Barrett, H. H.,“Spatial pileup considerations for pixelated gamma-ray detectors,” IEEETransactions on Nuclear Science, vol. 47, issue 4, pp. 1399-1403, August2000). Spatial pileup may introduce ambiguities in the way in whichflashes of lights are associated to different particles, potentiallyleading to inaccuracies during the estimation of position, energy anddirection of propagation. Fast cameras are advantageous for someapplications of the invention because, at high enough frame rates, theprobability of spatial pileup is negligible.

In embodiments, the processor comprises a computer, a computer, or otherhardware equivalent implementing a computer software. In embodiments,the particle-processing detector comprises a silicon sensor. Inembodiments, the device further comprising a tomographic imaging system.In embodiments, the particle-processing detector further comprises a GPU(graphics processing unit), an application-specific integrated circuit(ASIC), or a field-programmable gate array (FPGA). In an embodiment, theparticle-processing detector comprises a track detector.

In an exemplary embodiment, the two-dimensional detector comprises asemiconductor detector. In certain embodiments, the semiconductordetector provides an energy resolution equal to or better than 1% of thetotal energy deposited by the particle in the two-dimensional detector.In certain embodiments, the semiconductor detector provides a positionresolution equal to or better than 750 nm. In a specific embodiment, thetwo-dimensional detector comprises a hybrid semiconductor, pixelateddetector. In embodiments, the two-dimensional detector comprises a layerof semiconductor material comprising an active volume and a set ofanodes, wherein the set of anodes is provided in electrical contact witha side of the active volume opposite an entrance face of thetwo-dimensional detector. In embodiments, the semiconductor detector hasa 256×256 pixel array. In certain embodiments, the semiconductordetector has a detection area selected over the range of 100 mm² to 100cm². In embodiments, for example, the semiconductor detector provides asubpixel spatial resolution better than 750 nm for equivalent 10 MeValpha particle. In certain embodiments, the semiconductor detector isprovided proximate to the source of particles.

In some embodiments, the step of reconstructing the 3D distribution ofthe source of particles comprises calculating a probability densityfunction for each of a plurality of locations within the source ofparticles. In a specific embodiment, for example, the probabilitydensity function is calculated using a Monte Carlo simulation. Inembodiments, the probability density function for each location accountsfor propagation of particles between that location the two-dimensionaldetector.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A provides an overview of a method embodiment for determining orreconstructing a 3D distribution of a source of particles within atissue or object.

FIG. 1B provides an overview of a method embodiment for determining orreconstructing a 3D distribution of a source of particles within atissue or object.

FIG. 2 illustrates an exemplary embodiment of a system for determiningthe 3D reconstruction of a particle-generating object located within atissue.

FIG. 3A provides an exemplary 2D image associated with a multitude ofalpha particle interaction events (i.e., detections) with the detector.

FIG. 3B, provides blowup of a blob shown in FIG. 3A.

FIG. 3C provides a profile of the blob shown in FIG. 3B

FIG. 4 illustrates a simplified case of two alpha particles emitted by apoint-like radioactive source inside the tissue sample.

FIG. 5 provides an actual alpha particle emission pattern in slicedview.

FIG. 6 provides the reconstructed 3D distribution of the object shown inFIG. 5.

FIG. 7 provides a conventional alpha autoradiograph of the same objectis shown in FIG. 5.

FIG. 8 provides a top view line drawing of one embodiment of aparticle-processing detector.

FIG. 9A shows an image of a true decay pattern of one slice located atz=5.5 μm.

FIG. 9B shows an image of the same object of FIG. 9A reconstructed from10⁶ events of one slice located at z=5.5 μm.

FIG. 9C shows an image of a true decay pattern of one slice located atz=16.5 μm.

FIG. 9D shows an image of the same object of FIG. 9C reconstructed from10⁶ events of one slice located at z=16.5 μm

FIG. 10A shows Geant4-simulated tracks of ²³⁹Pu alpha particles inwater.

FIG. 10B shows the distribution of the angular deviation Δθ fromstraight line propagation. The angular deviation of Geant4 simulateddata has mean 1.00° and standard deviation 0.28°.

FIG. 10C illustrates the residual energy for ²³⁹Pu alpha particles inwater as a function of path length.

FIG. 10D illustrates the residual energy for ²³⁹Pu alpha particles inKapton as a function of path length.

FIG. 11A illustrates an exemplary embodiment of a system for determiningthe 3D reconstruction of a particle-generating object located within atissue comprising at least a CCD or CMOS detector and a scintillator.

FIG. 11B illustrates an exemplary embodiment of a system for determiningthe 3D reconstruction of a particle-generating object located within atissue comprising at least a CCD or CMOS detector, fiber optics, and ascintillator.

FIG. 11C illustrates an exemplary embodiment of a system for determiningthe 3D reconstruction of a particle-generating object located within atissue comprising at least a CCD or CMOS detector, a lens system, and ascintillator.

FIG. 11D illustrates an exemplary embodiment of a system for determiningthe 3D reconstruction of a particle generating object located within atissue comprising at least a CCD or CMOS detector, a microlens array,and a scintillator.

FIG. 12 illustrates an exemplary embodiment of a system for determiningthe 3D reconstruction of a particle generating object located within atissue comprising at least a PiN diode array detector, a light guide(glass spacer), and a scintillator.

FIG. 13 provides a schematic diagram illustrating a detectorconfiguration for track detection comprising a scintillator layer inoptical communication with a CMOS or CCD detector.

FIG. 14A illustrates a simulated ²³⁹Pu alpha decay in a 10 um YAG:Cescintillator with a Sony IMX 252 CMOS sensor.

FIG. 14B illustrates a contour plot and center line profiles of thesimulated ²³⁹Pu alpha decay in a 10 um YAG:Ce scintillator with a SonyIMX 252 CMOS sensor presented in FIG. 14A.

FIG. 15 provides a schematic diagram illustrating a detectorconfiguration for track detection comprising a semiconductor sensorhaving a depletion region.

FIG. 16 illustrates a detector configuration for track detectioncomprising a scintillator layer, high-NA Lenses or lenslet array and aCMOS or CCD detector.

FIG. 17 provides a schematic diagram illustrating detector configurationfor track detection comprising a scintillator layer, lenslet array and aCMOS or CCD detector.

FIG. 18A illustrates a detector configuration for track detectioncomprising a scintillator layer and a CMOS or CCD detector. Optionallythe detector configuration further comprises an air gap provided betweenscintillator layer and CMOS or CCD detector. The invention include,however, embodiments wherein there is no air gap provided betweenscintillator layer and CMOS or CCD detector.

FIG. 18B provides experimental data of beta particles on a CCD detectorwithout use of a scintillator.

FIG. 19 provides beta particle tracks detected using a WidePIX detector.The tracks can be analyzed and used for reconstruction of the sourcedistribution.

DETAILED DESCRIPTION

In general, the terms and phrases used herein have their art-recognizedmeaning, which can be found by reference to standard texts, journalreferences and contexts known to those skilled in the art. The followingdefinitions are provided to clarify their specific use in the context ofthe invention.

“Particle” refers to an object possessing mass. Particles aredistinguished from massless objects, such as photons. Exemplaryparticles include, but are not limited to, subatomic particles such asprotons, neutrons and electrons, high-energy particles such as alphaparticles and beta particles, atomic nuclei, atoms and ions. As usedherein, particles explicitly include beta particles, positrons,conversion electrons and Auger electrons.

“Alpha particle” refers to a particle comprising two protons and twoneutrons. Alpha particles are typically generated by the process ofradioactive decay, often referred to specifically as alpha decay. Asused herein, alpha particle refers to any particle consisting of twoprotons and two neutrons, regardless of energy or velocity.

“Two-dimensional detector” and “particle-processing detector” refer toan electronic device capable of measuring attributes, including theenergy and the 2-dimensional location of a particle, and otherattributes such as direction of a particle or time, at a point ofinteraction on a two-dimensional surface. Two-dimensional detectorsinclude silicon sensors, and scintillation cameras. Further, aparticle-processing detector is one that collects signals from multiplesensors (usually pixels) and uses them to estimate attributes of theparticle interaction

“Interaction” refers to a process where a particle's kinetic energy isreduced when it is exposed to or otherwise interacts with a material,device or device layer to generate a detectable signal, such aselectrons or photons.

“3D position” refers to a unique location within space characterized bythree coordinates, such as x, y, and z coordinates. In embodiments a 3Dposition can be provided by two coordinates (e.g., x and y) locatedwithin a plane or within a film or layer of material, and an intensityof the signal at the position provided by the two coordinates.

“2D position” refers to a unique location within plane characterized bytwo coordinates, such as x and y coordinates.

“Direction” refers to a description of the translation through space ofa particle. In embodiments, the direction of travel of a particle isspecified by two angles in a spherical coordinate system or by any twocomponents of a unit vector.

“Radiopharmaceutical” refers to a radioactive composition administeredto a subject or patient for use in the diagnosis, treatment, cure orprevention of a disease or condition or for use in imaging a tissue ortissue component. In embodiments, a radiopharmaceutical comprises one ormore radioisotopes that generate particles upon radioactive decay, suchas beta particles. In some embodiments, radiopharmaceuticals generategamma rays.

“Detectable signal” refers to charged particles, such as electrons, orelectromagnetic radiation that can be used for sensing the occurrence ofan interaction between a particle and an active material of a positionsensitive detector system.

“Semiconductor” refers to any material that is an insulator at very lowtemperatures, but which has an appreciable electrical conductivity attemperatures of about 300 Kelvin. In the present description, use of theterm semiconductor is intended to be consistent with use of this term inthe art of microelectronics and electrical devices. Typicalsemiconductors include element semiconductors, such as silicon orgermanium, and compound semiconductors, such as group IV compoundsemiconductors such as SiC and SiGe, group III-V semiconductors such asAlSb, AlAs, Aln, AlP, BN, GaSb, GaAs, GaN, GaP, InSb, InAs, InN, andInP, group III-V ternary semiconductors alloys such as AlxGa1−xAs, groupII-VI semiconductors such as CsSe, CdS, CdTe, ZnO, ZnSe, ZnS, and ZnTe,group I-VII semiconductors CuCl, group IV-VI semiconductors such as PbS,PbTe and SnS, layer-type semiconductors such as PbI₂, MoS₂ and GaSe,oxide semiconductors such as CuO, Cu₂O and TiO₂. The term semiconductorincludes intrinsic semiconductors and extrinsic semiconductors that aredoped with one or more selected materials, including semiconductorhaving p-type doping materials (also known as p-type or p-dopedsemiconductor) and n-type doping materials (also known as n-type orn-doped semiconductor), to provide beneficial electrical propertiesuseful for a given application or device. The term semiconductorincludes composite materials comprising a mixture of semiconductorsand/or dopants. Impurities of semiconductor materials are atoms,elements, ions and/or molecules other than the semiconductor material(s)themselves or any dopants provided to the semiconductor material. Inembodiments, an interaction between a semiconductor and a particle, suchas a beta particle, alpha particle, or conversion electron, generateselectron-hole pairs within the semiconductor. In embodiments, aninteraction between a semiconductor and a particle, such as a betaparticle, alpha particle or conversion electron, generates electron-holepairs that are separated within the depletion region of a semiconductordevice.

“List-mode maximum-likelihood expectation-maximization algorithm” or“LMMLEM algorithm” refers to method for image reconstruction. Anembodiment of this algorithm is described in L. Parra and H. H. Barrett,“List-mode likelihood—EM algorithm and noise estimation demonstrated on2D-PET,” IEEE Trans. Med. Imag. MI-17:228-235, 1998, which is herebyincorporated by reference.

“Sub-pixel” or “subpixel” are synonymous and refers to a high degree ofspatial resolution. In an embodiment of this invention, when the eventsare recorded with sufficient speed that it is possible to detect theinfluence of each interaction event on a plurality of detector elements,then one can use multiple pixel signals (from multiple electrodes) foreach interaction event to estimate the location of that event to anaccuracy that is less than the size of the electrode. It is alsopossible to estimate attributes other than the location for eachinteraction event. In some embodiments, depending on the size of thepixel, by fitting the signals to a Gaussian function, a detectorprovides sub-pixel spatial resolution to about 750 nm for equivalent 10MeV alpha particles. With bias voltage at 100 V, the energy resolutionis about 50 keV FWHM for 5.5 MeV alpha particles.

“Particle track” refers to the path of a particle through an activematerial, such as a scintillator or a microchannel plate or adeep-depletion CCD device or a deep-depletion CMOS device, along which adetectable signal is generated. A particle track generally begins at thepoint at which the particle enters the active material. In anembodiment, the particle track optionally ends when the particle exitsthe material. In an embodiment, the particle track optionally ends whenthe particle comes to a stop. A “particle track detector” refers to asystem for capturing a detectable signal generated as a particletraverses a path through an active material.

“Position dependent signal” refers to a signal generated by detection ormeasurement of a particle, such as a beta particle, alpha particle or aconversion electron, at a specific point on the trajectory of theparticle. In some embodiments, position dependent signals are useful forcharacterizing the trajectories of particle translating from a sourcethrough a detection region. Position dependent signals include opticalsignals, electronic signals, acoustic signals, magnetic signals, andcombinations of these.

“Active material” refers to a device, composition or structure thatgenerates, upon an interaction with a particle, a detectable signal thatoriginates from the specific location within the device, composition orstructure that the interaction occurs at.

“Scintillator,” “scintillation material” and “phosphor” refers to acomposition that emits photons upon an interaction with a particle, suchas a beta particle, alpha particle or conversion electron. Inembodiments, photons are emitted by these materials upon absorption of aparticle. In embodiments, photons are emitted by these materials whenthese materials interact with a particle and reduce the particle'skinetic energy.

“CCD” or “charge-coupled device” refers to an imaging device used fordetection of electromagnetic radiation by generation of and oraccumulation of charges upon absorption of electromagnetic radiation. Inembodiments, the term CCD refers to a two-dimensional array of CCDelements arranged to obtain an image.

“Deep-depletion CCD” refers to a specific CCD construction where thesemiconductor material comprising the active charge generation region ordepletion region is thicker than in a conventional CCD device such thatit permits detection of absorbed radiation or particles at depthsgreater than conventional a CCD. “Depletion region” refers to a regionof a CCD in which there is a high electric field for the purpose ofseparating electrons and holes. “CCD well” refers to a region of a CCDor deep-depletion CCD in which charges generated through the absorptionof electromagnetic are accumulated.

“CMOS sensor” refers to an imaging device used for detection ofelectromagnetic radiation. In embodiments, a CMOS sensor is fabricatedusing conventional methods and technology commonly known in the art ofmicrofabrication and integrated circuit fabrication as “complementarymetal-oxide-semiconductor.”

STATEMENTS REGARDING INCORPORATION BY REFERENCE AND VARIATIONS

All references throughout this application, for example patent documentsincluding issued or granted patents or equivalents; patent applicationpublications; and non-patent literature documents or other sourcematerial; are hereby incorporated by reference herein in theirentireties, as though individually incorporated by reference, to theextent each reference is at least partially not inconsistent with thedisclosure in this application (for example, a reference that ispartially inconsistent is incorporated by reference except for thepartially inconsistent portion of the reference).

All patents and publications mentioned in the specification areindicative of the levels of skill of those skilled in the art to whichthe invention pertains. References cited herein are incorporated byreference herein in their entirety to indicate the state of the art, insome cases as of their filing date, and it is intended that thisinformation can be employed herein, if needed, to exclude (for example,to disclaim) specific embodiments that are in the prior art. Forexample, when a compound is claimed, it should be understood thatcompounds known in the prior art, including certain compounds disclosedin the references disclosed herein (particularly in referenced patentdocuments), are not intended to be included in the claim.

When a group of substituents is disclosed herein, it is understood thatall individual members of those groups and all subgroups and classesthat can be formed using the substituents are disclosed separately. Whena Markush group or other grouping is used herein, all individual membersof the group and all combinations and subcombinations possible of thegroup are intended to be individually included in the disclosure. Asused herein, “and/or” means that one, all, or any combination of itemsin a list separated by “and/or” are included in the list; for example“1, 2 and/or 3” is equivalent to “‘1’ or ‘2’ or ‘3’ or ‘1 and 2’ or ‘1and 3’ or ‘2 and 3’ or ‘1, 2 and 3’”.

Every formulation or combination of components described or exemplifiedcan be used to practice the invention, unless otherwise stated. Specificnames of materials are intended to be exemplary, as it is known that oneof ordinary skill in the art can name the same material differently. Oneof ordinary skill in the art will appreciate that methods, deviceelements, starting materials, and synthetic methods other than thosespecifically exemplified can be employed in the practice of theinvention without resort to undue experimentation. All art-knownfunctional equivalents, of any such methods, device elements, startingmaterials, and synthetic methods are intended to be included in thisinvention. Whenever a range is given in the specification, for example,a temperature range, a time range, or a composition range, allintermediate ranges and subranges, as well as all individual valuesincluded in the ranges given are intended to be included in thedisclosure.

As used herein, “comprising” is synonymous with “including,”“containing,” or “characterized by,” and is inclusive or open-ended anddoes not exclude additional, unrecited elements or method steps. As usedherein, “consisting of” excludes any element, step, or ingredient notspecified in the claim element. As used herein, “consisting essentiallyof” does not exclude materials or steps that do not materially affectthe basic and novel characteristics of the claim. Any recitation hereinof the term “comprising”, particularly in a description of components ofa composition or in a description of elements of a device, is understoodto encompass those compositions and methods consisting essentially ofand consisting of the recited components or elements. The inventionillustratively described herein suitably may be practiced in the absenceof any element or elements, limitation or limitations that are notspecifically disclosed herein.

The terms and expressions which have been employed are used as terms ofdescription and not of limitation, and there is no intention in the useof such terms and expressions of excluding any equivalents of thefeatures shown and described or portions thereof, but it is recognizedthat various modifications are possible within the scope of theinvention claimed. Thus, it should be understood that although thepresent invention has been specifically disclosed by preferredembodiments and optional features, modification and variation of theconcepts herein disclosed may be resorted to by those skilled in theart, and that such modifications and variations are considered to bewithin the scope of this invention as defined by the appended claims.

The invention may be further understood by the following non-limitingexamples.

EXAMPLE 1 Autoradiography Methods

Autoradiography is the use of a radioactive pharmaceutical to studyclinical or biological processes. The radiation source is inside theobject being studied, and the prefix ‘auto’ distinguishes it fromconventional radiography where an external radiation source is used.Sometimes SPECT (single-photon emission computed tomography) and PET(positron emission tomography) tomography which also use internalradioactive sources, are referred to as in vivo autoradiography, but theterm is used much more commonly to refer to ex vivo imaging of a tissuespecimen after a biopsy of a patient or in an animal imaging study afterthe animal is sacrificed.

In these procedures, the radiopharmaceutical is introduced into theliving subject, and after a suitable time for it to equilibrate, thespecimen is removed and cut with a device called a microtome into verythin slices, often only 5-10 μm thick. Each slice is then placed over ahigh-resolution imaging detector which is sensitive to chargedparticles, such as alpha particle, beta particles or Auger electrons,that are emitted by the radioactive isotope used in the pharmaceutical.Depending on the isotope, there may also be x-ray or gamma rayemissions, which can be used for in vivo tomography, but the imagingdetectors used in ex vivo autoradiography are designed to be relativelyinsensitive to these photon emissions.

The resulting 2D autoradiographic slice images can have exquisitespatial resolution, far better than that of SPECT or PET; they candisplay the details of the radiopharmaceutical distribution at acellular or subcellular level, but of course only after the specimen isno longer part of a living subject. In principle, the 2D slice imagescan also be assembled into a 3D image, analogous to those produced bySPECT and PET, but in practice this procedure is both laborious andtechnically challenging. The technical challenges stem from distortionsintroduced by the transfer of tissue from the microtome and the imagingdetector and/or the tissue dehydration process.

One goal achieved by the present invention is extension of 2Dautoradiography to 3D, such that the full volumetric distribution of thepharmaceutical is imaged without having to reassemble the 3D volume fromdistorted 2D slices.

A second goal achieved by the present invention is obtaining the 3Dimage with a detector in contact or near contact with just one face ofthe tissue being imaged, rather than surrounding the tissue withdetectors as in SPECT or PET.

A further goal is achieved by the present invention is achieving the twogoals with very high spatial resolution, much better than in SPECT orPET, rivaling that of thin-slice autoradiography.

These goals give 3D autoradiography uses for in vivo imaging, not justex vivo.

An aspect of one embodiment of the invention is the use ofcharged-particle detectors that provide information about not only thelocation of the particle when it interacts with the detector but alsoits direction. With photon detectors, as in SPECT and PET, there is nopossibility of learning anything about the direction of the photon froma single interaction with the detector. A high-energy photon travelsunimpeded through a detector until it makes a Compton or photoelectricinteraction at a single point; in a scintillation detector, eachinteraction produces a single flash of light. A high-energy chargedparticle, on the other hand, interacts with the detector all along itspath. In a semiconductor detector, the position and energy of eachdetected alpha particle is measured.

As discussed below, this example describes algorithms to determine theposition and direction of an alpha particle at the point it enters thedetector. This information is stored about each particle, for example,in a list, 3D (or higher dimensions) grid or other database, and it isused, along with a sophisticated particle transport algorithm, toreconstruct the 3D distribution of the radioactive pharmaceutical.

Major advantages achieved by the embodiments described herein includethe ability to produce high-resolution 3D imaging of the distribution ofa radioactive pharmaceutical in a tissue without physically slicing itinto thin sections. In addition, this technique is applicable tovirtually any radioisotope.

3D tomography with a detector on only one side of the tissue can beachieved by the techniques described herein. In addition, the techniquesdescribed herein are applicable to living tissue, for example with skinlesions or epithelial lesions accessible with endoscopy. Furthermore,dynamic (4D) studies on living subjects can be achieved.

EXAMPLE 2 Real-Time Maximum-Likelihood (ML) Methods and ReconstructionFunctions

The manuscripts J. Y. Hesterman, L. Caucci, M. A. Kupinski, H. H.Barrett and L. R. Furenlid, “Maximum-likelihood estimation with acontracting-grid search algorithm,” IEEE Trans. Nucl. Sci., 57(3),1077-1084 2010, and A. K. Jha, H. H. Barrett, E. C. Frey, E. Clarkson,L. Caucci, and M. A. Kupinski, “Singular Value Decomposition forphoton-processing nuclear imaging systems and applications forreconstruction and computing null functions,” Phys. Med. Biol. 60 (2015)7359-7385 discusses methods and functions and is hereby incorporated byreference.

EXAMPLE 3 Overview of a Method for Reconstructing 3D Image

FIG. 1A provides an overview of a method embodiment for determining orreconstructing a 3D distribution of a source of particles within atissue or object. Initially, (110) particles are emitted from a sample.The particles can be beta particles, alpha particles, positrons, orconversion electrons. Next, (120) for a plurality of particles emittedfrom the sample, the particles are detected by a particle-processingdetector in electrical communication with a GPU. The GPU) produces oneor more 2D images, in which each image contains zero or more blobs,where each blob corresponds to the data from all pixels that producedsignals from one particle interacting in the detector. This process isrepeated for each of plurality of particles from the source. Next, (130)each blob is fitted with an appropriate function (such as a Gaussian).This fitting step is carried out by a first computer program thatimplements a maximum likelihood (ML) algorithm. Determined Attributes ofthe particles, including at least: a two-dimensional positioncorresponding to an interaction point where the particle interacts withthe particle-processing detector (raw Attributes includes an x positionand a y position), and an energy that is deposited in theparticle-processing detector characterize the fittings. The DeterminedAttributes for each particle are stored in in an attribute list, 3D gridof bins or a database. Next, (140) the Determined Attributes (stored inin an attribute list, 3D grid of bins or a database) are operated uponby a second computer program to reconstruct a 3D distribution of theemitted particles. This second computer program performs thereconstruction via the maximum-likelihood expectation-maximization(MLEM) algorithm. Optionally, (150) the 3D reconstruction of the sourceof the particles can by visually displayed.

As an example, we refer to the process of scanning a frame of data anddetermining which pixels have contributions from one particleinteraction as “frame parsing.” Frame parsing indeed produces the blobin the present method, and in may be performed by an ASIC(application-specific integrated circuit), or more commonly a GPU(graphics processing unit) or an FPGA (field-programmable gate array).In some embodiments, GPUs and FPGAs have the advantage of beingprogrammable, while an ASIC is a fixed configuration of electronicgates. To go a step further, the same GPU can be used for real-timeattribute estimation for one event, given the data from the pixelswithin that blob. In some embodiments, the process is ML estimation,however, not simple Gaussian fitting; the key difference is that MLErequires a model for the statistics of the data. A reference describingthis approach is Hesterman et al.: J. Y. Hesterman, L. Caucci, M. A.Kupinski, H. H. Barrett and L. R. Furenlid, “Maximum-likelihoodestimation with a contracting-grid search algorithm,” IEEE Trans. Nucl.Sci., 57(3), 1077-1084 2010. PMC2932457, which is incorporated byreference in its entirety.

FIG. 1B provides an overview of a method for determining orreconstructing a 3D distribution of a source of particles within atissue or object includes the following. Initially, (110)autoradiography particles (1000) are emitted from the source of chargedparticles randomly with the time between the emission of one particleand the next particle occurring according a known probability densityfunction. The probability density function of the emission of chargedparticles is well known to those skilled in the art. Next, (120) theemitted particles are detected and imaged (1200) by theparticle-processing detector for a period of time. The imaging initiatesat a time T1 and continues until imaging concludes at a time T2. Duringthis time interval, N images (1200) are collected by the detector. Someparticles are detected by the detector and they will produce blobs inthe images (1200). Some images (1200) might have blobs while some otherimages (1200) might have no blobs. Each detected particle will produceone blob in one of the N images (1200). Next, (130) each blob is fittedwith an appropriate function (such as a Gaussian). This fitting step iscarried out by a first computer program that implements a maximumlikelihood (ML) algorithm. The fittings are characterized by DeterminedAttributes of the particles, including at least: a two-dimensionalposition (raw Attributes includes an x position and a y position), andan energy. Next (140) the Determined Attributes for each particle arestored in an attribute list, 3D grid of bins or a database (1400). Next,(150) the Determined Attributes (stored in in an attribute list, 3D gridof bins or a database) are operated upon by a second computer program toreconstruct a 3D distribution of the emitted particles. This secondcomputer program performs the reconstruction via the maximum-likelihoodexpectation-maximization (MLEM) algorithm. Optionally, (160) the 3Dreconstruction of the source of the particles can by visually displayed.

EXAMPLE 4 Alpha-Particle Emission Tomography (αET)

Targeted alpha-particle therapy has advantages over beta-particletherapy for treatment of malignant disease. The range of alpha particlesin tissue is short, with little radiation dose to surrounding non-targettissues, and the linear energy transfer is high, resulting incytotoxicity for the target tissue. Targeted alpha imaging and therapyare promising for localizing and eliminating minimal residual diseaseand micrometastases, which if not ablated will lead to tumor relapse.

Because alpha particles lose energy approximately in proportion to theamount of tissue they traverse, the energy deposited in the detector byan alpha particle allows quantification of the path length the alphaparticle traveled in the tissue. In an embodiment, the particle's energyis deposited on the detector, along with the position of interaction ofthe particle with the detector's entrance face to introduce new imagingmethods and reconstruction algorithms applicable to alpha-particletherapy and imaging.

System Description

In an embodiment, semiconductor detectors that measure position as wellas energy of each detected alpha particle are used. In addition toposition, energy provides depth information about the object, thusmaking a 3D reconstruction of the object feasible.

FIG. 2 illustrates an exemplary embodiment of a system 200 fordetermining the 3D reconstruction of a particle-generating object 210located within a tissue (220). System 200 comprises a 300 μm siliconsensor 230 and a readout GPU (graphics processing unit) 240. Particlesgenerated by the particle-generating object travel along paths 250through the tissue 220, where they optionally undergo absorption andscattering. When the particles reach the silicon sensor 230,interactions along paths 250 between the particles and the sensor 230generate electromagnetic radiation which is collected by the GPU chip

In an embodiment of the invention, the imaging system includes a hybridsemiconductor pixelated detector to directly sense alpha particles. Onepossible detector configuration includes a layer of semiconductormaterial (which we will refer to as the “detector's active volume”), aset of anodes placed on one side of the detector's active volume, andsome data-processing circuitry (such as a GPU (graphics processingunit)) that acquires the anodes signals and convert them into pixelcounts (or any other suitable data format). Although not limited to thecase of the aforedescribed detector, an embodiment of the setup shown inFIG. 2.

A requirement for the detector is that it must provide accurate 2Dposition information as well as good energy resolution. The sampletissue being imaged is placed in contact or in close proximity to thedetector's active volume face opposite to the anodes, as shown in FIG.2. Geometric parameters of the detector (such as the material making upthe detector's active volume, thickness and surface area of the activevolume, bias voltage—if any—applied across the detector's active volume,number of anodes and their spacing/arrangement, data format for thedetector output, etc.) are design parameters of the detector. Thesedesign parameters, which will affect the statistics of the data in acomplicated way, are assumed known and will be supplied to the datareconstruction algorithm (or any other computer code that processesdetector data).

Alpha particles emitted upon radioactive decay occurring in the tissueto be imaged will typically travel no more than 10 μm within thedetector's active volume. Upon interaction of an alpha particle insidethe detector's active volume, the alpha particle's residual energy willyield a shower of electron-hole pairs inside the detector's activevolume. The number of electron-hole pairs generated is proportional tothe particle's residual energy. The bias voltage applied across thedetector's active volume forces the electrons towards the anodes, wherethey get collected. This process induces a current in each anode that isproportional to the number of electrons reaching the anode. Currentsgenerated at the anodes are measured and converted to computer-readabledata by the detector. Thus, in a cascading effect, alpha particlesemitted within the tissue sample produce data that are acquired andprocessed by a computer.

An exemplary 2D image is shown in FIG. 3A, and can be associated to amultitude of alpha particle interactions (i.e., detection) with thedetector. For each “blob” shown in FIG. 3A, a 2D position can becalculated and related to the 2D location at which the alpha particleentered the detector's active volume. Furthermore, the sum of the pixelintensities associated to any given blob is proportional to theparticle's residual energy. The proportionality constant can becalculated from the detector properties (such as material, bias voltage,etc.). A blowup of a blob shown in FIG. 3A is presented in FIG. 3B, anda profile of the blob shown in FIG. 3B is presented in FIG. 3C.

Besides 2D position and particle residual energy, other quantities (suchas time and direction of travel) can be estimated as well. In anembodiment, the set of parameters estimated for each particle isidentified as the “estimated parameter vector.” The estimated parametervector of each alpha particle is stored in a list, and this list is usedto reconstruct the 3D distribution of the radioactive pharmaceutical.This data arrangement is referred to as “list-mode.”

Data Processing and Reconstruction

Because the alpha particle's initial energy (i.e., the energy of theparticle emitted upon radioactive decay) is known, knowledge of theparticle's residual energy as it enters the detector face allowscalculation of the energy the particle lost while traveling within thetissue. In turn, from the amount of energy lost by the particle, thetotal distance the particle traveled within the tissue can becalculated.

As shown in FIG. 4, the 2D location at which the particle enters thedetector and the distance the particle has traveled in the tissue definea spherical shell in object space (i.e., the tissue). The modelconstrains the point at which the alpha particle was emitted to lay onthis spherical shell. In the simplified case of all alpha particlesemitted from a point-like region in the tissue, the spherical shellswill all intersect at the emission point.

FIG. 4 shows simplified case of two alpha particles emitted by apoint-like radioactive source inside the tissue sample. The 2D locationof interaction for the first alpha particle is (x₁,y₁) and theparticle's residual energy is E₁. The 2D location of interaction for thesecond alpha particle is (x₂,y₂) and the particle's residual energy isE₂. In our example, E₁<E₂, which implies that the first particle hastraveled inside the tissue a longer distance than the second particlehas (the different size of the sparkles located at (x₁,y₁) and (x₂,y₂)has been chosen to reflect the fact that E₁<E₂. The two spherical shellsthat denote the possible source locations for each particle intersect atthe location of the point-like radioactive source.

Given a list of estimated parameter vectors, a method of imagereconstruction for this problem is the list-mode maximum-likelihoodexpectation-maximization (LMMLEM) algorithm. For the purpose ofdiscussing the reconstructing algorithm and simulation results, it willbe assumed that each estimated parameter vector contains estimates({circumflex over (x)},ŷ) of the 2D location of interaction on thedetector's face, as well as an estimate Ê of the particle's energy as itenters the detector.

A preliminary study indicated that alpha-particle emission tomography(αET) reconstruction can achieve a resolution of 1-2 μm or better acrossa relatively large field of view. In the simulation study, positionresolution of the detector is assumed to be 750 nm, and the energyresolution to be 1% of the total energy deposited. FIG. 5 shows theactual alpha particle emission pattern in sliced view and thereconstructed 3D object is shown in FIG. 6.

As a comparison, a conventional alpha autoradiograph of the same objectis shown in FIG. 7.

EXAMPLE 5 An Embodiment of an αET System

Introduction

Alpha particles have desirable properties for radionuclide therapy,including localized deposition of energy with sparing of nearby tissues,provided that the alpha particles are guided specifically to theirtarget. Antibodies to tumor antigens as well as peptides targeting tumorcell-surface receptors can provide this specific targeting. Studies ofantibodies labeled with alpha emitters such as ²¹¹At, ²¹³Bi and ²²⁵Acfor cancer treatment are currently in progress.

Accurate radiation dose estimation for alpha emitters requires knowledgeof not only whole-body biodistribution but also cellular-leveldistribution of the alpha particles. Knowledge of cellular distributionwithin the tumor will enable prediction of the radiation dose to thetumor, and knowledge of localization during excretion, such as thespecific cellular localization in the kidneys, will predict whethertoxic effects will result.

When alpha emissions are accompanied by gamma or x-ray photons, standardimaging methods can be used, but the usual limitations on spatialresolution will apply. When no photons are present, direct alpha imagingis required.

The distinctive physical properties of alpha particles enable αET.Unlike beta particles, alpha particles have discrete energy spectra,with highly monoenergetic emission lines associated with particularnuclear transitions. In low-atomic-number materials such as water ortissue, alpha particles interact with matter primarily through Coulombforces between their positive charges and the negative charges of theorbital electrons within the absorber atoms. At any given time, theparticle is interacting with many electrons, so the net effect is todecrease its velocity continuously until the particle is stopped. Exceptat their very end, the tracks tend to be quite straight because theparticle is not significantly deflected by any one encounter, andinteractions are statistically uniform in all directions. The distancean alpha particle travelled is therefore characterized by the energydeposited in a given absorber material [1]. Hence, the path length is afunction of the particle residual energy. When an alpha particle emittedin a homogeneous medium is detected at location (x,y) with energy E, thesource is restricted to a spherical shell centered at (x,y) with radiusdetermined by E, as illustrated in FIG. 4.

FIG. 4 provides an illustration of an αET system with anenergy-sensitive imaging detector. Alpha particles emitted within thetissue propagate along straight lines. From each measured position({circumflex over (x)}_(d),ŷ_(d)) and energy Ê of an alpha particleleaving the tissue, one can localize the emission event to a sphericalshell. A 3D reconstruction can be achieved from a list of detectedevents.

System Configuration

An exemplary αET imaging system includes a hybrid semiconductor pixeldetector to directly sense alpha particles. A requirement for thedetector is that it provides accurate position information as well asgood energy resolution. Semiconductor detectors allow for good energyresolution because the average energy necessary to create anelectron-hole pair is smaller than that needed for other types ofcharged particle detectors. An embodiment is illustrated in FIG. 2: Thedetector is placed in contact or in close proximity with a sampletissue. Alpha particles emitted by a radioactive isotope inside thetissue are detected by the silicon sensor and produce measurable signalsin the detector.

FIG. 8 provides a top view line drawing of one embodiment of aparticle-processing detector. The hybrid semiconductor pixel detector(ModPIX; WidePIX company) in the system has 256×256 pixels, each 55μm×55 μm. The detecting area of the silicon sensor 230 is about 14 mm×14mm. The large number of electron-hole pairs produced upon interaction ofan alpha particle with the detector material gives rise to pixelintensities that approximate a 2D Gaussian function. By fitting thesignals to a Gaussian function, the detector provides sub-pixel spatialresolution to about 750 nm for equivalent 10 MeV alpha particles [2].With bias voltage at 100 V, the energy resolution is about 50 keV FWHMfor 5.5 MeV alpha particles [3].

Forward Model

A continuous-to-continuous model of an αET system with kernelh(x_(d),y_(d), E; x,y,z) that maps an object f(x,y,z) into estimateddata g(x_(d),y_(d),E) can be described as [4]:g({circumflex over (r)} _(d))=∫_(V) _(f) d ³ R∫ _(S) _(d) dx _(d) dy_(d)∫₀ ^(∞) dE h(r _(d) ;R)pr({circumflex over (r)} _(d) |r_(d))f(R),  (1)

where V_(f) is the object space; S_(d) is the detector surface;{circumflex over (r)}_(d) denotes estimated attributes ({circumflex over(x)}_(d),ŷ_(d),Ê) and f(R)=f (x,y,z) refers to the density of the alpharadioactive tracer, measured in 1/mm³. The function on the left-handside of this equation g({circumflex over (r)}_(d)), is the density ofdetected alpha particles that pass through tissue and enter the detectorat estimated position ({circumflex over (x)}_(d),ŷ_(d)) with estimatedenergy Ê; the units of g({circumflex over (r)}_(d)) are 1/(μm²·MeV). Thefactor pr({circumflex over (r)}_(d)|r_(d)) characterizes the ability ofthe detector and electronics to perform the estimation. The kernelh(r_(d); R) is the ideal response of the system at r_(d) to a deltafunction of activity at point R in object space. Alpha particles travelthrough tissue in a nearly straight path with energy decreasingcontinuously. The energy E of an alpha particle as a function of thedistance l the particle traveled has been described by aStar [5]. Thisallows us to use the estimated detection location ({circumflex over(x)}_(d),ŷ_(d)) and the estimated alpha particle energy Ê to restrictthe origin of the alpha particle to the vicinity of a spherical shellcentered at ({circumflex over (x)}_(d),ŷ_(d)). For αET, the kernel inequation (1) is:

$\begin{matrix}{{h\mspace{11mu}\left( {x_{d},y_{d},{E;x},y,z} \right)} = {\frac{z}{4{\pi\ell}^{3}}{\delta\left( {E - {E(\ell)}} \right)}}} & (2)\end{matrix}$

where l=√{square root over ((x_(d)−x)²+(y_(d)−y)²+z²)} is the distancefrom the emission point of an alpha particle to the position where itwas detected. We assumed the detector is located at z=0. Thesensitivity, which is the probability of a decay at position (x,y,z)being detected, is given by

$\begin{matrix}{{{s\left( {x,y,z} \right)} = {\frac{1}{2}\left( {1 - \frac{z}{\ell_{0}}} \right)}},\mspace{20mu}\left( {z \leq \ell_{0}} \right),} & (3)\end{matrix}$

where l₀ is the range of an alpha particle in a given material, which isrelated with alpha decay energy of the source. For a 5.2445 MeV alphaparticle (²³⁹Pu), l₀ is approximately 40 μm in soft tissue.

If maximum-likelihood methods are used to estimate {circumflex over (r)}from pixel intensities, then the probability density functionpr({circumflex over (r)}|r), which describes how well r is estimatedfrom the detector outputs, asymptotically approximates a multivariatenormal PDF with mean r [6]. In addition, by taking advantage oftranslational symmetry, the detector response is shift invariant inareas excluding detector boundaries. Therefore, pr({circumflex over(r)}_(j)|n), the probability of measuring {circumflex over(r)}_(j)=({circumflex over (x)}_(j),ŷ_(j),Ê_(j)) when a particle isemitted from voxel n, is approximated as

$\begin{matrix}\begin{matrix}{{{pr}\left( {{\hat{r}}_{j},{{\hat{E}}_{j}❘n}} \right)} = {{pr}\left( {0,0,{{\hat{E}}_{j}❘m}} \right)}} \\{{\approx {{\quad\frac{d\;\ell}{dE}}_{{\hat{E}}_{j}}\frac{1}{V}\underset{V_{m}}{\int{\int\int}}\frac{z}{4\pi\; R^{3}}{G\left( {{R;{\hat{\ell}}_{j}},\sigma_{j}} \right)}d^{3}R}},}\end{matrix} & (4)\end{matrix}$

where V is voxel volume and the m-th voxel, V_(m), corresponds to V_(n)after shifting r_(j) to the origin. G(R;{circumflex over (l)}_(j),σ_(j))is a one-dimensional Gaussian function with mean {circumflex over(l)}_(j) and standard deviation σ_(j) both depending on Ê_(j).

Reconstruction with Expectation Maximization Algorithm

The Expectation Maximization (EM) algorithm is an iterative algorithm tosolve inverse problem. The list-mode EM algorithm takes the form [6]:

$\begin{matrix}{{\hat{f}}_{n}^{({k + 1})}{\hat{f}}_{n}^{(k)}\left\{ {\frac{1}{V\mspace{11mu} S_{n}}{\sum\limits_{j = 1}^{J}\frac{{pr}\left( {{\hat{r}}_{j}❘n} \right)}{\sum\limits_{n^{\prime} = 1}^{N}{{{pr}\left( {{\hat{r}}_{j}❘{n\prime}} \right)}{\hat{f}}_{n}^{(k)}}}}} \right\}} & (5)\end{matrix}$

A slab of 1 mm×1 mm×50 μm tissue with 1 μm³ cubic voxels is discretized.Assuming position standard deviation equals to 320 nm, and energyresolution 1% of the energy detected, Geant4-simulated data arereconstructed according to equation (5). FIG. 9 shows the central partof slices of a reconstructed 3-D object compared with the true decaypattern. The true decay pattern is contrasted with an objectreconstructed from 10⁶ events. The object consists of two layers,located at z=6 μm and z=16 μm, each 4 μm thick. FIG. 9A shows an imageof a true decay pattern of one slice located at z=5.5 μm. FIG. 9B showsan image of the same object reconstructed from 10⁶ events of one slicelocated at z=5.5 μm. Similarly, FIG. 9C shows an image of a true decaypattern of one slice located at z=16.5 μm, while FIG. 9D shows an imageof the same object reconstructed from 10⁶ events of one slice located atz=16.5 μm. Each slice is 1 μm thick.

For comparison, a conventional alpha autoradiograph is shown in FIG. 7of the same simulated object in FIG. 9. The information of the objectlocated deeper in the tissue is buried by the strong signal fromshallower object.

Alpha Emission Tomography (αET) is an imaging modality that produces athree-dimensional image of the distribution of alpha-particle-emittingradioisotope sources. A system configuration and a mathematical forwardmodel are described. An Expectation Maximization reconstructionalgorithm is introduced. The simulation results show that in addition toposition, energy information makes a three-dimensional reconstruction ofan alpha radioactive distribution possible. In simulations, theresolution of the system is on the scale of 1 μm. Alpha EmissionTomography has the potential to achieve imaging of sample tissue withsubcellular resolution. In real experiments, the detector responsevaries from pixel to pixel

REFERENCES

[1] G. F. Knoll, Radiation detection and measurement. Wiley, 2010.

[2] J. Jakubek, A. Cejnarova, T. Holy, S. Pospisil, J. Uher, and Z.Vykydal, “Pixel detectors for imaging with heavy charged particles,”Nucl. Instr. and Meth. A, 2008.

[3] C. Granja, P. Krist, D. Chvatil, J. Solc, S. Pospisil, J. Jakubek,and L. Opalka, “Energy loss and online directional track visualizationof fast electrons with the pixel detector timepix,” RadiationMeasurements, 2013.

[4] H. H. Barrett and K. J. Myers, Foundations of image science, Wiley,New York, 2004.

[5] M. J. Berger, J. Coursey, M. Zucker, and J. Chang, Stopping-powerand range tables for electrons, protons, and helium ions. NIST PhysicsLaboratory, 1998.

[6] L. Caucci, L. Furenlid, and H. Barrett, “Maximum likelihood eventestimation and list-mode image reconstruction on GPU hardware,” inNuclear Science Symposium Conference Record (NSS/MIC), 2009 IEEE, 2009.

EXAMPLE 6 Physics

Unlike beta particles, alpha sources emit particles with a discretespectrum. Alpha particles interact with matter primarily through Coulombforces between their positive charge and the negative charge of theorbital electrons within the absorber atoms. Except at their very end,the tracks (particle trajectories) tend to be quite straight, as shownin FIG. 10A-D. FIG. 10A shows Geant4-simulated tracks of ²³⁹Pu alphaparticles in water. FIG. 10B shows Δθ as the angular deviation fromstraight line propagation. The angular deviation of Geant4 simulateddata has mean 1.00° and standard deviation 0.28°. FIGS. 10C and 10D showthe residual energy for ²³⁹Pu alpha particles in water and Kapton,respectively, as a function of path length.

REFERENCES

M. J. Berger, J. Coursey, M. Zucker, and J. Chang, Stopping-power andrange tables for electrons, protons, and helium ions. NIST PhysicsLaboratory, 1998.

L. Caucci, L. Furenlid, and H. Barrett, “Maximum likelihood eventestimation and list-mode image reconstruction on GPU hardware,” in IEEENuclear Science Symposium Conference Record (NSS/MIC), 2009.; and J. Y.Hesterman, L. Caucci, M. A. Kupinski, H. H. Barrett and L. R. Furenlid,“Maximum-likelihood estimation with a contracting-grid searchalgorithm,” IEEE Trans. Nucl. Sci., 57(3), 1077-1084 2010. PMC2932457

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EXAMPLE 7 Additional Embodiments of Detector Systems

FIG. 11A illustrates an exemplary embodiment of a scintillation camerafor charged particles comprising at least a CCD or CMOS detector and ascintillator.

FIG. 11B illustrates an exemplary embodiment of a scintillation camerafor charged particles comprising at least a CCD or CMOS detector, fiberoptics, and a scintillator.

FIG. 11C illustrates an exemplary embodiment of a scintillation camerafor charged particles comprising at least a CCD or CMOS detector, a lenssystem, and a scintillator.

FIG. 11D illustrates an exemplary embodiment of a scintillation camerafor charged particles comprising at least a CCD or CMOS detector, amicrolens array, and a scintillator.

FIG. 12 illustrates an exemplary embodiment of a scintillation camerafor charged particles comprising at least a PiN diode array detector, alight guide (glass spacer), and a scintillator.

EXAMPLE 8 Particle Detection and Characterization Using Track Detection

Some of the systems and methods of the invention may also be implementedusing track detection to provide additional particle characterizationcomplementary to measurements of 2D position and energy deposited in thedetector. Suitable track detectors, for example, includescintillator-based detectors, microchannel plate-based imageintensifiers coupled to a thick scintillation material, CMOS detectorsand CCD detectors. In certain embodiments, for example, the trackdetector measures a detected image corresponding to particle trajectory,such as an elongated image, which is analyzed to determine attributes ofthe particle such as the 2D position at which the charged particleentered the detector, the particle's direction at that point (or otherpoints along the particle trajectory in the detector) and/or the totalenergy deposited in the detector. Use of track detection in theinvention, therefore, provides supplemental characterization of particledirection, in addition to of 2D position and energy deposited in thedetector, thereby providing additional particle attributes allowing formore accurate and/or efficient reconstruction of the distribution of asource of the particles. In an embodiment, for example, the detectorconfiguration provides a measurement of the direction of the particle atthe point of interacting with the detector, for example, comprising twoor more angles characterizing a direction of travel along the particletrajectory.

In some embodiments, for example, detected particles interact with anactive material, such as a light-emitting or electron-emitting material,so as to generate a track of interactions corresponding to the particletrajectory within the track detector component. The invention includesembodiments, for example, wherein: (i) the thickness of the activematerial (e.g., light-emitting or electron-emitting material) isselected such that the particle does not traverse the entire thicknessof the track detector component or, alternatively, (ii) the thickness ofthe active material (e.g., light-emitting or electron-emitting material)is thin enough such that the particle does traverse the entire thicknessand, optionally impinges directly on a sensor component of the detectorconfiguration; e.g., a CMOS, CDD, or other semiconductor detector.

Detection and characterization of the image of generated by the particleinteracting with the active material (e.g., light-emitting orelectron-emitting material) at multiple points along the track allowsfor determination of the direction of travel of the particle, optionallyrepresented as one or more angles, which may be also added to a list, 4Dgrid or other database for later reconstruction of the distribution ofsource of the particle. In a specific embodiment, for example, a methodof this aspect further comprises the steps of: repeating, for each of aplurality of particles from the source, the steps of: a) recording animage of a particle track with a particle track detector; b) determiningattributes of the particle track (e.g., 2D position, direction,trajectory, energy deposited in detector, etc.) using the particle trackimage; and c) storing the attributes of the particle track; therebygenerating additional attributes for each of the plurality of particlesfrom the source. In an embodiment, the attributes of the particle trackare determined to within the uncertainty of a selected analyticalapproach, such as a list-mode maximum likelihoodexpectation-maximization algorithm. In an embodiment, the attributes ofthe particle track are estimated, for example, using an approximateanalysis technique or predictive algorithm.

FIG. 13 provides a schematic diagram illustrating a detectorconfiguration for track detection comprising a scintillator layer inoptical communication with a CMOS or CCD detector. As shown in thisfigure, a thin layer of scintillator (e.g., 5 μm to 20 μm thick) isprovided in close proximity (e.g., with 10 mm or optionally within 1 mm)to the CMOS or CCD detector (or other light sensitive detector). Forsome applications, the pixel size of the detector is small enough suchthat light from the particle track is imaged on a plurality of pixels soas to provide a detected image characterized by a shape, such as anelongated shape. Analysis of the shape of the image in some methods andsystems provides supplemental information for characterizing attributesthe particle, such as the incident direction of the charged particle, aswell as the 2D incident position and the energy deposited in thedetector.

As shown in this figure, an alpha particle incident to the detector hasa trajectory passing through the scintillator, thereby producingscintillation light points along the track. In the embodiment depictedin FIG. 13, the thickness of the scintillator is large enough such thatthe alpha particle does not entirely pass through the scintillator. Asshown in the figure, light originating from interaction points along thetrack spreads out as a function of distance from the interaction points,thereby generating a more diffuse distribution of radiant intensities.The irradiance on the sensor, therefore, is characterized in someembodiments by an elongated shape, which provides information about the2D incident position and direction of the charged particle. Thecumulative radiant flux provides complementary information useful forcharacterizing the energy of the charged particle.

FIG. 14A illustrates a simulated ²³⁹Pu alpha decay for a detectorconfiguration comprising a 10 um YAG:Ce scintillator in opticalcommunication with a Sony IMX 252 CMOS sensor. FIG. 14B illustrates acontour plot and center line profiles of the center of the simulated²³⁹Pu alpha decay in a 10 um YAG:Ce scintillator with a Sony IMX 252CMOS sensor presented in FIG. 14A.

FIG. 15 provides a schematic diargam illustrating a detectorconfiguration for track detection comprising a semiconductor sensorhaving a depletion region. As shown in FIG. 15, an incident alphaparticle interacts with the depletion region (e.g. thickness equal to 5μm-50 μm) of a sensor having a pixel pitch selected from the range of 1μm to 20 μm, thereby generating charges along its track. The shape ofthe detected image provides information about the direction of the alphaparticle along the particle trajectory and the cumulative intensity ofthe detected image provides information relating to the energy of thealpha particle. In an embodiment, detection parameters including pixelsize and density are selected to allow characterization of the shape ofthe detected image to allow for characterization of particle direction.

In some embodiments, detector configurations providing track detectionmay further incorporate optical components such as lenses or an array oflenslets to collect and focus light generated upon a particle passingthrough a scintillator layer onto a 2D optical sensor such as a CMOS ora CCD detector.

FIG. 16 provides a schematic illustration of a detector configurationfor track detection comprising a scintillator layer, high-NA Lenses orlenslet array and a CMOS or CCD detector. As shown in this figure, analpha particle incident into the scintillator layer producesscintillation light along a track corresponding to the trajectory of theparticle within the scintillator. In the embodiment depicted in FIG. 16,the thickness of the scintillator is large enough that the alphaparticle does not entirely pass through the scintillator. As shown inthe figure, light originating from interaction points along the trackspreads out as a function of distance from the interaction points,thereby generating a more diffuse distribution of radiant intensities.Light from the scintillator is collected by a lens system and imaged onthe CMOS or CCD sensor, thereby generating an image characterized by anelongated shape. Analysis of the elongated image provides forcharacterization of the incident angle, direction and deposited energyof the particle. FIG. 17 illustrates a detector configuration for trackdetection comprising a scintillator layer, lenslet array and a CMOS orCCD detector.

In some embodiments, the detector configuration for track detection isarranged such that particles directly interact with both an activematerial (e.g., light-emitting or electron-emitting material) and asensor component (e.g., optical, opto-electronic and/or electronicsensor). This aspect of the invention may be particularly well-suitedfor characterization of particles that are more efficiently andnondestructively transported through components of the detector, such asbeta particles. FIG. 18A illustrates a detector configuration for trackdetection comprising a scintillator layer and a CMOS or CCD detector.Optionally the detector configuration further comprises an air gapprovided between scintillator layer and CMOS or CCD detector. Theinvention include, however, embodiments wherein there is no air gapprovided between scintillator layer and CMOS or CCD detector.

As shown in FIG. 18A, a beta particle penetrates the entire thickness ofthe scintillator and further directly interacts with the sensor. In thisembodiment, the detected image resulting from transport of the particlewithin the detector is a composite signal comprising a signal componentgenerated by interaction of particle with the scintillator layer and asignal component generated by interaction of particle with the CMOS, CCDor other detector. For example, by combining the signal from thescintillation light and from direct interaction with the CMOS, CCD orother detector, the direction and/or the energy of the particle may beaccurately characterized. FIG. 18B provides experimental data of betaparticles on a CCD detector without use of a scintillator. FIG. 19illustrates beta particle tracks detected using a WidePIX detector. Thetracks can be analyzed and used for reconstruction of the sourcedistribution.

We claim:
 1. A method for reconstructing a 3D distribution of a sourceof particles, the method comprising the steps of: providing the sourceof particles from within a tissue sample, wherein the particles comprisebeta particles, alpha particles, positrons, or conversion electrons;repeating, for each of a plurality of the particles from the source, thesteps of: detecting the particle with a particle-processing detector;determining attributes of the particle; wherein the attributes comprisea two dimensional position corresponding to an interaction point withina plane where the particle interacts with the particle-processingdetector, and at least one of the following: (i) an energy that isdeposited in the particle-processing detector by the particle; and (ii)a direction of travel of the particle at the interaction point where theparticle interacts with the particle-processing detector; and storingthe attributes of the particle; thereby generating attributes for eachof the plurality of particles from the source; and reconstructing the 3Ddistribution of the source of particles using at least a portion of theattributes for each of the plurality of particles.
 2. The method ofclaim 1, wherein the particle is emitted upon radioactive decayoccurring in the tissue to be imaged.
 3. The method of claim 1, whereinthe particle-processing detector comprises a track detector.
 4. Themethod of claim 3, wherein the track detector comprises a processor fordetermining attributes using a maximum-likelihood algorithm.
 5. Themethod of claim 1, wherein the particles are alpha particles and themethod further comprises calculating an energy lost for each of theplurality of particles while traveling in the tissue sample andcalculating a distance traveled for each of the plurality of particleswithin the tissue sample from the energy lost by each of the particles.6. The method of claim 1, wherein the attributes further comprise aparticle interaction time.
 7. The method of claim 1, further comprisinga step of administering the source of particles to a patient, subject ortissue, wherein the source of particles comprises one or more of aradiopharmaceutical, a radioisotope or a radiotracer.
 8. The method ofclaim 1, wherein the particle-processing detector comprises asemiconductor detector, wherein the semiconductor detector provides anenergy resolution equal to or better than 10% of the total energydeposited by the particle in the particle-processing detector.
 9. Themethod of claim 8, wherein the semiconductor detector provides aposition resolution equal to or better than 10 μm.
 10. The method ofclaim 8, wherein the particle-processing detector comprises a layer ofsemiconductor material comprising an active volume and a set of anodes;wherein the set of anodes is provided in electrical contact with a sideof the active volume opposite an entrance face of theparticle-processing detector.
 11. The method of claim 1, wherein theparticle-processing detector comprises a processor for determining theattributes using a maximum-likelihood algorithm.
 12. The method of claim1, wherein the particle-processing detector comprises a scintillationcamera or a silicon sensor.
 13. The method of claim 12, wherein thescintillation camera comprises a scintillation crystal, a PiN diodearray, and a light guide wherein the light guide is a glass spacer. 14.The method of claim 13, wherein the scintillation camera furthercomprises a charge-coupled device (CCD), or a complementarymetal-oxide-semiconductor (CMOS) detector.
 15. The method of claim 1,wherein the attributes further comprise one or more additionalattributes including at least a portion of: a time, and one or moreangles characterizing a direction of travel along the independentparticle trajectory at the 2D position.
 16. The method of claim 1,wherein the attributes comprise a set of parameters that are estimatedfor each particle as an estimated parameter vector and stored as entriesin an attribute list, 3D grid of bins or a database.
 17. The method ofclaim 16, wherein the estimation of the set of parameters is performedusing a maximum-likelihood search algorithm.
 18. The method claim 1,wherein the step of reconstructing the 3D distribution is performedusing a list-mode maximum-likelihood expectation-maximization algorithm.19. The method of claim 1, wherein the step of reconstructing the 3Ddistribution of the source of particles comprises calculating aprobability density function for each of a plurality of locations withinthe source of particles.
 20. The method of claim 1, wherein the step ofreconstructing the 3D distribution of the source of particles is carriedout using an Ordered Subsets-Expectation Maximization (OSEM) algorithm,an Algebraic Reconstruction Technique (ART), or a Simultaneous IterativeReconstructive Technique (SIRT).
 21. The method of claim 1, furthercomprising providing a visual display of the 3D distribution of thesource of particles.
 22. The method of claim 1, further comprising, forat least a portion of said particles, independently measuring an imagecorresponding to a track of said particle interacting with theparticle-processing detector.
 23. The method of claim 22, furthercomprising, for at least a portion of said particles, determining one ormore additional attributes of each of said particles using said imagecorresponding to said track of the particle, wherein said one or moreadditional attributes are selected from the group consisting of a 2Dposition of a start of said particle track, a direction of travel of aparticle at a point along said particle track, and a total energydeposited by a particle along a particle track.
 24. The method of claim1, wherein the attributes comprise each of the following: a twodimensional position corresponding to an interaction point within aplane where the particle interacts with the particle-processingdetector; an energy that is deposited in the particle-processingdetector by the particle; and a direction of travel of the particle atthe interaction point where the particle interacts with theparticle-processing detector.
 25. A device for reconstructing a 3Ddistribution of a source of particles from within a tissue sample,wherein the particles comprise beta particles, alpha particles,positrons, or conversion electrons, the device comprising: aparticle-processing detector for detecting particles; a processorpositioned in data communication with the particle-processing detector,wherein the processor is configured for: determining attributes of theparticle; wherein the attributes comprise a two dimensional positioncorresponding to an interaction point within a plane where the particleinteracts with the particle-processing detector, and at least one of thefollowing: (i) an energy that is deposited in the particle-processingdetector by the particle; and (ii) a direction of travel of the particleat the interaction point where the particle interacts with theparticle-processing detector; and storing the attributes of theparticle; thereby generating attributes for each of the plurality ofparticles from the source; and reconstructing the 3D distribution of thesource of particles using at least a portion of the attributes for eachof the plurality of particles.
 26. The device of claim 25, wherein theparticle-processing detector comprises a silicon sensor or ascintillation camera.
 27. The device of claim 25, wherein theparticle-processing detector further comprises a tomographic imagingsystem, a GPU, FPG or an application-specific integrated circuit (ASIC).28. The device of claim 25, wherein the particle-processing detectorcomprises a track detector.
 29. The device of claim 25, wherein theattributes comprise each of the following: a two dimensional positioncorresponding to an interaction point within a plane where the particleinteracts with the particle-processing detector; an energy that isdeposited in the particle-processing detector by the particle; and adirection of travel of the particle at the interaction point where theparticle interacts with the particle-processing detector.
 30. A devicefor reconstructing a 3D distribution of a source of particles fromwithin a tissue sample, wherein the particles comprise beta particles,alpha particles, positrons, or conversion electrons, the devicecomprising: a particle-processing detector for detecting particles; aprocessor positioned in data communication with the particle-processingdetector, wherein the processor is configured for: determiningattributes of the particle; wherein the attributes comprise a twodimensional position corresponding to an interaction point within aplane where the particle interacts with the particle-processingdetector, and at least one of the following: (i) an energy that isdeposited in the particle-processing detector by the particle; and (ii)a direction of travel of the particle at the interaction point where theparticle interacts with the particle-processing detector; and storingthe attributes of the particle; thereby generating attributes for eachof the plurality of particles from the source; and reconstructing the 3Ddistribution of the source of particles using at least a portion of theattributes for each of the plurality of particles.
 31. The device ofclaim 30, wherein the attributes comprise each of the following: a twodimensional position corresponding to an interaction point within aplane where the particle interacts with the particle-processingdetector; an energy that is deposited in the particle-processingdetector by the particle; and a direction of travel of the particle atthe interaction point where the particle interacts with theparticle-processing detector.